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PP-Structure Quick Start

1. Install package

# Install paddleocr, version 2.5+ is recommended
pip3 install "paddleocr>=2.5"
# Install the DocVQA dependency package paddlenlp (if you do not use the DocVQA, you can skip it)
pip install paddlenlp

2. Use

2.1 Use by command line

2.1.1 image orientation + layout analysis + table recognition

paddleocr --image_dir=PaddleOCR/ppstructure/docs/table/1.png --type=structure --image_orientation=true

2.1.2 layout analysis + table recognition

paddleocr --image_dir=PaddleOCR/ppstructure/docs/table/1.png --type=structure

2.1.3 layout analysis

paddleocr --image_dir=PaddleOCR/ppstructure/docs/table/1.png --type=structure --table=false --ocr=false

2.1.4 table recognition

paddleocr --image_dir=PaddleOCR/ppstructure/docs/table/table.jpg --type=structure --layout=false

2.1.5 DocVQA

Please refer to: Documentation Visual Q&A .

2.2 Use by code

2.2.1 image orientation + layout analysis + table recognition

import os
import cv2
from paddleocr import PPStructure,draw_structure_result,save_structure_res

table_engine = PPStructure(show_log=True, image_orientation=True)

save_folder = './output'
img_path = 'PaddleOCR/ppstructure/docs/table/1.png'
img = cv2.imread(img_path)
result = table_engine(img)
save_structure_res(result, save_folder,os.path.basename(img_path).split('.')[0])

for line in result:
    line.pop('img')
    print(line)

from PIL import Image

font_path = 'PaddleOCR/doc/fonts/simfang.ttf' # PaddleOCR下提供字体包
image = Image.open(img_path).convert('RGB')
im_show = draw_structure_result(image, result,font_path=font_path)
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')

2.2.2 layout analysis + table recognition

import os
import cv2
from paddleocr import PPStructure,draw_structure_result,save_structure_res

table_engine = PPStructure(show_log=True)

save_folder = './output'
img_path = 'PaddleOCR/ppstructure/docs/table/1.png'
img = cv2.imread(img_path)
result = table_engine(img)
save_structure_res(result, save_folder,os.path.basename(img_path).split('.')[0])

for line in result:
    line.pop('img')
    print(line)

from PIL import Image

font_path = 'PaddleOCR/doc/fonts/simfang.ttf' # PaddleOCR下提供字体包
image = Image.open(img_path).convert('RGB')
im_show = draw_structure_result(image, result,font_path=font_path)
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')

2.2.3 layout analysis

import os
import cv2
from paddleocr import PPStructure,save_structure_res

table_engine = PPStructure(table=False, ocr=False, show_log=True)

save_folder = './output'
img_path = 'PaddleOCR/ppstructure/docs/table/1.png'
img = cv2.imread(img_path)
result = table_engine(img)
save_structure_res(result, save_folder, os.path.basename(img_path).split('.')[0])

for line in result:
    line.pop('img')
    print(line)

2.2.4 table recognition

import os
import cv2
from paddleocr import PPStructure,save_structure_res

table_engine = PPStructure(layout=False, show_log=True)

save_folder = './output'
img_path = 'PaddleOCR/ppstructure/docs/table/table.jpg'
img = cv2.imread(img_path)
result = table_engine(img)
save_structure_res(result, save_folder, os.path.basename(img_path).split('.')[0])

for line in result:
    line.pop('img')
    print(line)

2.2.5 DocVQA

Please refer to: Documentation Visual Q&A .

2.3 Result description

The return of PP-Structure is a list of dicts, the example is as follows:

2.3.1 layout analysis + table recognition

[
  {   'type': 'Text',
      'bbox': [34, 432, 345, 462],
      'res': ([[36.0, 437.0, 341.0, 437.0, 341.0, 446.0, 36.0, 447.0], [41.0, 454.0, 125.0, 453.0, 125.0, 459.0, 41.0, 460.0]],
                [('Tigure-6. The performance of CNN and IPT models using difforen', 0.90060663), ('Tent  ', 0.465441)])
  }
]

Each field in dict is described as follows:

field description
type Type of image area.
bbox The coordinates of the image area in the original image, respectively [upper left corner x, upper left corner y, lower right corner x, lower right corner y].
res OCR or table recognition result of the image area.
table: a dict with field descriptions as follows:
        html: html str of table.
        In the code usage mode, set return_ocr_result_in_table=True whrn call can get the detection and recognition results of each text in the table area, corresponding to the following fields:
        boxes: text detection boxes.
        rec_res: text recognition results.
OCR: A tuple containing the detection boxes and recognition results of each single text.

After the recognition is completed, each image will have a directory with the same name under the directory specified by the output field. Each table in the image will be stored as an excel, and the picture area will be cropped and saved. The filename of excel and picture is their coordinates in the image.

/output/table/1/
  └─ res.txt
  └─ [454, 360, 824, 658].xlsx        table recognition result
  └─ [16, 2, 828, 305].jpg            picture in Image
  └─ [17, 361, 404, 711].xlsx        table recognition result

2.3.2 DocVQA

Please refer to: Documentation Visual Q&A .

2.4 Parameter Description

field description default
output result save path ./output/table
table_max_len long side of the image resize in table structure model 488
table_model_dir Table structure model inference model path None
table_char_dict_path The dictionary path of table structure model ../ppocr/utils/dict/table_structure_dict.txt
merge_no_span_structure In the table recognition model, whether to merge '<td>' and '</td>' False
layout_model_dir Layout analysis model inference model path None
layout_dict_path The dictionary path of layout analysis model ../ppocr/utils/dict/layout_publaynet_dict.txt
layout_score_threshold The box threshold path of layout analysis model 0.5
layout_nms_threshold The nms threshold path of layout analysis model 0.5
vqa_algorithm vqa model algorithm LayoutXLM
ser_model_dir Ser model inference model path None
ser_dict_path The dictionary path of Ser model ../train_data/XFUND/class_list_xfun.txt
mode structure or vqa structure
image_orientation Whether to perform image orientation classification in forward False
layout Whether to perform layout analysis in forward True
table Whether to perform table recognition in forward True
ocr Whether to perform ocr for non-table areas in layout analysis. When layout is False, it will be automatically set to False True
recovery Whether to perform layout recovery in forward False
structure_version Structure version, optional PP-structure and PP-structurev2 PP-structure

Most of the parameters are consistent with the PaddleOCR whl package, see whl package documentation